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Key Responsibilities:

• Develop models, perform data analysis, and generate insights to support product and business decisions.
• Collaborate with cross-functional stakeholders to define key business questions and project goals.
• Gather, extract, and clean data from multiple sources using tools such as SQL, R, or Python and

validate data to ensure quality, and review the dataset to ensure it is ready for analysis.

• Design and evaluate predictive models and machine learning algorithms to solve business challenges.
• Present findings in a clear and concise manner to both technical and non-technical audiences.

Projects Expectations:

• Improve Workspace EDU Upsell Propensity Model for International Markets. We developed a Workspace EDU upsell propensity for North America (NORTHAM) that identifies customers with a high likelihood of upgrading from a free EDU sku to a paid version. We plan to extend this model for scoring EDU International customers. There's an opportunity to enhance the methodology by incorporating additional data signals and developing a machine learning model tailored to the international markets. A related project here would be to conduct an A/B test to measure the impact of the current version of the propensity model in free to paid conversions for the international markets. Additionally test if models built for specific international markets outperforms NORTHAM model applied globally.

• Build ChromeOS Enterprise Upsell model. We're currently developing a propensity model to improve the productivity of ChromeOS ADRs (sales development reps). The model will help us prioritize account assignments for the ADRs based on the customers' propensity to adopt ChromeOS as their main enterprise IT mgmt platform. Over time, we would want to extend the model to improve upsell of ChromeOS within the existing ChromeOS customers
  • E.g. a customer with 1000 employees adopts ChromeOS but activates the product for only 500 employees; upselling here means to increase ChromeOS adoption to all 1000 employees

• • The propensity model will help identify customers which are more likely to respond positively to the upsell pitch

• Enhance ChromeOS Online Sales Program. Online sales is a key New Business acquisition channel for ChromeOS. Overall, the SMB New Business is currently growing at 18% YoY, whereas, the target growth rate is 23%. At the moment, direct online sales have a strong momentum, growing 23% YoY. We believe we can leverage this momentum to increase the overall SMB NB sales growth rate to >= 23% YoY
  • BAU, customers sign up for the ChromeOS trial product (which is free) via the online sales platform. We then monitor those customers and try to predict which of these customers would convert to a paid SKU organically vs which customers need a sales touch point for conversion

• • With only 1 FTE managing online sales, we must prioritize which customers we should touch. However, predicting these customers is quite hard

• • The propensity model (leveraging both 1p and 3p data sources) would help us identify which customers should we prioritize for the sales touch point